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Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Work...最新文献

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Artificial Intelligence and the Control of COVID-19: A Review of Machine and Deep Learning Approaches 人工智能与COVID-19的控制:机器和深度学习方法综述
S. Folorunso, E. Ogbuju, Francisca Onaolapo Oladipo
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引用次数: 0
Applications of Artificial Intelligence and Molecular Immune Pathogenesis, Ongoing Diagnosis and Treatments for COVID-19 人工智能与COVID-19分子免疫发病机制、持续诊断和治疗的应用
Balendra V. S. Chauhan, A. Jaiswar, A. Bedi, Sneha Verma, Vivek Kumar Shrivastaw, Ajitanshu Vedrtnam
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引用次数: 0
Spread of COVID-19 in Odisha (India) Due to Influx of Migrants and Stability Analysis Using Mathematical Modeling 移民涌入导致的COVID-19在印度奥里萨邦的传播和使用数学模型的稳定性分析
A. Rauta, Yerra Shankar Rao, Jangyadatta Behera
This paper deals with the investigation on spread of COVID-19 and its stability analysis (both local and global stability) in Odisha, India. Being the second most populous country in the world, It is urgent need to investigate the spread and control of disease in India .However, due to diversity of vast population, uncertainty of infection, varying rate of recovery, state wise different COVID-19 induced death rate and non uniform quarantine policy of the states, it is strenuous to predict the spread and control of disease accurately in the country. So, it is crucial to study the aspects of disease in each state for the better prediction. We have considered the state Odisha (India) having population nearly equal to the population of Spain because the entry of huge migrants to the state suddenly enhanced the number of COVID-19 patients from below two hundred to more than eight hundred within one week even after forty days of lockdown period. We have developed SIAQR epidemic model fabricated with influx of out-migrants diagnosed at compartment (A) , then sent to the compartment (I) for treatment those have confirmed the disease and the remaining healthy individuals are sent to quarantine compartment (Q) for a period of twenty one days under surveillance and observation. The set of ordinary (nonlinear) differential equations are formulated and they are solved using Runge -Kutta fourth order method. The simulation of numerical data is performed using computer software MATLAB. As there is no specific treatment, vaccine or medicine available for the disease till the date, so the only intervention procedure called quarantine process is devised in this model to check the stability behaviour of the disease. The numerical and analytical results of the study show that the disease free equilibrium is locally stable when basic reproduction number is less than unity and unstable when it is more than unity. Further the study shows that it persists to endemic equilibrium for global stability when basic reproduction number greater than unity. As per the current trends ,this study shows that the prevalence of COVID -19 would remain nearly 250 to 300 days in Odisha as for as the infected migrants would have been entering to the state. This mathematical modelling embedded with important risk factor like migration could be adopted for each state that will be helpful for better prediction of the entire country and world.
本文研究了2019冠状病毒病在印度奥里萨邦的传播情况及其稳定性分析(包括局部稳定性和全球稳定性)。作为世界上人口第二多的国家,印度迫切需要调查疾病的传播和控制。然而,由于人口的多样性,感染的不确定性,不同的恢复速度,不同的州不同的COVID-19诱导死亡率和各州不统一的检疫政策,要准确预测疾病在该国的传播和控制是很困难的。因此,为了更好地预测,研究每个州的疾病方面是至关重要的。我们认为印度奥里萨邦的人口几乎与西班牙人口相当,因为大量移民进入该邦,即使在40天的封锁期后,一周内COVID-19患者人数也从不足200人突然增加到800多人。我们建立了SIAQR流行病模型,将在(A)隔间确诊的外来移民流入,然后送到(I)隔间进行治疗,将确诊的健康个体送到(Q)隔离隔间进行为期21天的监测和观察。建立了一组常(非线性)微分方程,并用Runge -Kutta四阶方法求解。利用MATLAB软件对数值数据进行了仿真。由于到目前为止还没有特定的治疗方法、疫苗或药物,因此在该模型中设计了唯一的干预程序——检疫过程,以检查疾病的稳定性行为。数值分析结果表明,当基本繁殖数小于1时,无病平衡是局部稳定的,大于1时,无病平衡是不稳定的。进一步的研究表明,当基本繁殖数大于1时,其维持地方性平衡以保持全局稳定。根据目前的趋势,这项研究表明,COVID -19的流行将在奥里萨邦持续近250至300天,因为受感染的移民将进入该邦。这种嵌入了移民等重要风险因素的数学模型可以适用于每个州,这将有助于更好地预测整个国家和世界。
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引用次数: 3
期刊
Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Work...
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